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Smoothing Methods in Statistics

  • Jeffrey S. Simonoff

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Jeffrey S. Simonoff
    Pages 1-12
  3. Jeffrey S. Simonoff
    Pages 13-39
  4. Jeffrey S. Simonoff
    Pages 40-95
  5. Jeffrey S. Simonoff
    Pages 96-133
  6. Jeffrey S. Simonoff
    Pages 134-214
  7. Jeffrey S. Simonoff
    Pages 215-251
  8. Jeffrey S. Simonoff
    Pages 252-274
  9. Back Matter
    Pages 275-339

About this book

Introduction

The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth­ ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan­ tage of this, they will argue.

Keywords

Estimator Excel Likelihood Projection Pursuit best fit correlation statistical software

Authors and affiliations

  • Jeffrey S. Simonoff
    • 1
  1. 1.Department of Statistics and Operations Research, Leonard N. Stern School of BusinessNew York UniversityNew YorkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-4026-6
  • Copyright Information Springer-Verlag New York 1996
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8472-7
  • Online ISBN 978-1-4612-4026-6
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
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